1. Field of Invention
The invention pertains to the fields of pattern recognition, scene segmentation, machine vision and machine speech recognition, and neuromorphic engineering. The invention constitutes a group of elementary circuits composed of interconnected e-cells (the subject of a related application) that themselves can be assembled into a new class of systems to implement perceptual and cognitive functions in various sensory and cognitive domains.
2. Discussion of Related Art
The history of neurologically inspired computing technology goes back at least several decades. The field lay relatively quiescent until the mid 1980s when the popularization of the multi-layer "artificial neural net" (ANN), usually emulated in software, overcame a decade and a half of skepticism about the technology largely engendered by an influential book "Perceptrons" by Marvin Minsky and Seymour Papaert. The multilayer ANN, particularly ones trained by various algorithms of backward propagation of an error signal, proved capable of a wide range of classification tasks. Though the differences between ANNs and biological neural circuitry was evident, the lack of an alternative computational hypothesis for the biological circuitry at first attracted some in the neuroscience community to adopt the ANN in its various forms as a model of neural computation. The limitations of the ANN, however, became apparent in both technological application and as a computational model for the brain. The ANN has become an accepted part of the computational repertoire for solving a certain classes of classification problems and problems involving capturing of complex functions from a large set of training data.
However, the ANN on its own has not provided the basis for solutions to the more complex problems of vision, speech recognition and other sensory and cognitive computation. Problems of segmentation, recognition of signals under various transformations, learning from single or very limited presentations of data, regularity extract, and many other real world recognition problem have to date eluded solution by ANN or any other neurally inspired techniques.
Experimental neuroscience has revealed that cortical and other neural architecture is far more complicated than any ANN. Realistic simulations of large neurons such as pyramidal or Purkinje cells suggest that individual neurons are capable of significant computation on their own, which must be the basis of the computations performed by circuits containing myriads of such cells. Experiment has also determined that connectivity between neurons is highly specific and becomes more so during the initial learning by organisms. Neurons themselves are specialized, not uniform. Strong evidence exists that synchronized oscillations across the cortices play a role in recognition. And perhaps most important is the fact that backward or descending signal paths are at least as numerous as the forward or ascending signal paths from sensory organs to progressively "higher" cortices.